Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	27,116
2	Arizona	25,036
3	Florida	23,407
4	New York	21,740
5	Mississippi	21,317
6	New Jersey	20,848
7	Alabama	19,304
8	South Carolina	18,542
9	Rhode Island	18,389
10	District of Columbia	17,630
11	Georgia	17,555
12	Massachusetts	17,358
13	Nevada	17,180
14	Texas	16,581
15	Tennessee	16,298
16	Delaware	15,708
17	Maryland	15,382
18	Arkansas	15,339
19	Illinois	14,873
20	Iowa	14,844
21	Nebraska	14,210
22	Connecticut	14,087
23	California	13,483
24	Utah	13,249
25	Idaho	12,788
26	North Carolina	12,348
27	Virginia	11,135
28	Indiana	10,648
29	Wisconsin	10,519
30	South Dakota	10,363
31	Kansas	10,313
32	New Mexico	10,285
33	Minnesota	10,252
34	Oklahoma	10,249
35	Michigan	9,416
36	Pennsylvania	9,411
37	North Dakota	9,265
38	Missouri	9,111
39	Colorado	8,533
40	Ohio	8,238
41	Washington	8,193
42	Kentucky	7,570
43	Puerto Rico	6,153
44	Alaska	5,715
45	Wyoming	5,050
46	New Hampshire	4,941
47	Oregon	4,736
48	Montana	4,192
49	West Virginia	3,994
50	Maine	2,969
51	Vermont	2,301
52	Hawaii	1,935

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Louisiana	444
2	Mississippi	323
3	Texas	316
4	Nevada	286
5	Georgia	277
6	Arkansas	274
7	Idaho	259
8	Florida	242
9	South Carolina	238
10	Tennessee	219
11	Alabama	218
12	Oklahoma	198
13	Kansas	195
14	Missouri	182
15	North Dakota	173
16	Arizona	172
17	Rhode Island	144
18	California	142
19	Kentucky	138
20	Nebraska	135
21	Utah	135
22	North Carolina	132
23	Puerto Rico	129
24	Wisconsin	129
25	Virginia	127
26	Illinois	125
27	Hawaii	122
28	Maryland	119
29	Delaware	118
30	Iowa	117
31	Minnesota	108
32	Indiana	106
33	Washington	97
34	Ohio	93
35	Alaska	90
36	Montana	89
37	New Mexico	87
38	South Dakota	80
39	District of Columbia	79
40	Colorado	77
41	Michigan	73
42	Oregon	69
43	Wyoming	66
44	Pennsylvania	63
45	Massachusetts	57
46	West Virginia	56
47	Connecticut	38
48	New Jersey	35
49	New York	33
50	New Hampshire	20
51	Maine	8
52	Vermont	5

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,783
2	New York	1,667
3	Massachusetts	1,256
4	Connecticut	1,244
5	Rhode Island	955
6	Louisiana	881
7	District of Columbia	831
8	Michigan	648
9	Illinois	613
10	Mississippi	606
11	Delaware	602
12	Maryland	584
13	Pennsylvania	571
14	Arizona	540
15	Indiana	446
16	Georgia	367
17	South Carolina	367
18	Florida	355
19	Alabama	345
20	Colorado	322
21	New Mexico	318
22	New Hampshire	307
23	Ohio	307
24	Minnesota	296
25	Nevada	289
26	Iowa	285
27	Texas	281
28	Virginia	266
29	California	249
30	Washington	223
31	Missouri	217
32	North Carolina	198
33	Nebraska	176
34	Kentucky	172
35	Arkansas	168
36	Wisconsin	168
37	Tennessee	165
38	South Dakota	154
39	Oklahoma	147
40	North Dakota	146
41	Kansas	127
42	Idaho	121
43	Utah	102
44	Maine	92
45	Vermont	91
46	Oregon	81
47	Puerto Rico	77
48	West Virginia	69
49	Montana	60
50	Wyoming	46
51	Alaska	31
52	Hawaii	18

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Mississippi	11
2	Florida	8
3	Arizona	7
4	South Carolina	7
5	Texas	7
6	Louisiana	6
7	Nevada	6
8	Alabama	4
9	Arkansas	4
10	Georgia	4
11	California	3
12	Idaho	3
13	Tennessee	3
14	Iowa	2
15	New Mexico	2
16	North Carolina	2
17	Oklahoma	2
18	Virginia	2
19	Illinois	1
20	Indiana	1
21	Kansas	1
22	Maryland	1
23	Massachusetts	1
24	Missouri	1
25	Montana	1
26	North Dakota	1
27	Ohio	1
28	Puerto Rico	1
29	Rhode Island	1
30	Utah	1
31	Washington	1
32	West Virginia	1
33	Wisconsin	1
34	Alaska	0
35	Colorado	0
36	Connecticut	0
37	Delaware	0
38	District of Columbia	0
39	Hawaii	0
40	Kentucky	0
41	Maine	0
42	Michigan	0
43	Minnesota	0
44	Nebraska	0
45	New Hampshire	0
46	New Jersey	0
47	New York	0
48	Oregon	0
49	Pennsylvania	0
50	South Dakota	0
51	Vermont	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	139,667	1	99
Lake	Tennessee	108,039	2	99
Lee	Arkansas	100,485	3	99
Dakota	Nebraska	95,276	4	99
Lincoln	Arkansas	91,830	5	99
Davidson	Tennessee	31,689	129	95
Richland	South Carolina	20,069	378	87
Orange	California	12,007	880	71
York	South Carolina	11,891	894	71
Pierce	Washington	6,465	1631	48

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,020	1	99
Randolph	Georgia	3,836	2	99
Galax city	Virginia	3,781	3	99
Terrell	Georgia	3,517	4	99
McKinley	New Mexico	3,111	5	99
Richland	South Carolina	356	654	79
Davidson	Tennessee	300	766	75
Orange	California	209	1009	67
Pierce	Washington	151	1264	59
York	South Carolina	100	1544	50

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.